Regional budswelling data

Description

A netCDF containing the DOY (day of year) for budswelling in the year 2023 provided by the Agricultural meteorology Department of the DWD.

Format

netCDF

Details

The dataset can be loaded using load_test("budswelling").

Spatial resolution: 1 km x 1 km Projection: DHDN / 3-degree Gauss-Kruger zone 3 (EPSG:31467) Parameter: DOY for first day of budswelling

Calculate degree days using the single sine method of Baskerville

Description

The function is vectorized. To use it with SpatRaster data see ?terra::lapp

More Information about the Method itself: Baskerville, G.L. and P. Emin. 1969. Rapid estimation of heat accumulation from maximum and minimum temperature. Ecology 50:514-517.

Usage

calc_baskerville(tmin, tmax, ldt)

Arguments

Value

Degree days calculated by using the single sine method of Baskerville.

See Also

Other Temperature sum methods: calc_tsum()

Calculate degree days

Description

Calculate degree days

Usage

calc_degreedays(x, ts_start, ts_end, ldt, method)

Arguments

Value

SpatRaster of growing (summed up) degree days.

See Also

Other Models: calc_mortality(), calc_phenology(), calc_sumefftemp()

Calculate first TRUE

Description

Takes multiple logical SpatRasters with time attribute and returns SpatRaster with common time and TRUE if any SpatRaster is TRUE.

Usage

calc_first(...)

Arguments

Value

SpatRaster - logical - with time attribute

See Also

Other Calculation: calc_last(), calc_mean()

Calculate last TRUE

Description

Takes multiple logical SpatRasters with time attribute and returns SpatRaster with time serial number of last TRUE layer time.

Usage

calc_last(x)

Arguments

Value

SpatRaster with time serial number (origin = lubridate::origin) of last TRUE layer.

See Also

Other Calculation: calc_first(), calc_mean()

Calculate mean TRUE

Description

Takes multiple logical SpatRasters with time attribute and returns SpatRaster with the mean time serial number of first and last TRUE value.

Usage

calc_mean(x)

Arguments

Value

SpatRaster - logical - with time attribute

See Also

Other Calculation: calc_first(), calc_last()

Calculate mortality

Description

Calculate mortality

Usage

calc_mortality(x, budswelling, hatch, mot = 0)

Arguments

Value

SpatRaster of starving related mortality in %.

See Also

Other Models: calc_degreedays(), calc_phenology(), calc_sumefftemp()

Calculate phenological models

Description

This function is checking the dependencies of the model and dependent on them:

1 Calculate the needed sum of effective (SET) temperatures for the phenological event to happen 2 Calculate the summed degree days (depending on hatch or not) 3 Check if the summed degree days have reached the needed sum of effective temperatures

Usage

calc_phenology(x, params)

Arguments

Value

SpatRaster - logical - with event occurred/not TRUE/FALSE

See Also

Other Models: calc_degreedays(), calc_mortality(), calc_sumefftemp()

Calculate sum of effective temperatures (SET)

Description

Calculate sum of effective temperatures (SET)

Usage

calc_sumefftemp(x, cf_temp, cf_start, cf_end, cf_limit, set, a, b)

Arguments

Value

SpatRaster with sum of effective temperatures.

See Also

Other Models: calc_degreedays(), calc_mortality(), calc_phenology()

Calculate temperature sum

Description

The function is vectorized. To use it with SpatRaster-Data see ?terra::lapp.

Usage

calc_tsum(tmean, ldt)

Arguments

Value

The temperature sum over the lower development threshold.

See Also

Other Temperature sum methods: calc_baskerville()

Call a function from PHENTHAUproc package

Description

calls a function from PHENTHAUproc package

Usage

call_function(f, data, params)

Arguments

See Also

Other Helper: check_data_with_params(), check_dimension_and_time(), convert_dwd_to_phenthau(), get_date(), get_formalArgs(), get_time(), load_test(), remove_false(), set_attributes(), subset_time(), timename()

Check data for model requirements

Description

Check data for model requirements

Usage

check_data_with_params(x, params)

Arguments

Value

No return value, called for side effects.

See Also

Other Helper: call_function(), check_dimension_and_time(), convert_dwd_to_phenthau(), get_date(), get_formalArgs(), get_time(), load_test(), remove_false(), set_attributes(), subset_time(), timename()

Check dimension and time attribute of a SpatRaster list

Description

Check dimension and time attribute of a SpatRaster list

Usage

check_dimension_and_time(...)

Arguments

Value

No return value, called for side effects.

See Also

Other Helper: call_function(), check_data_with_params(), convert_dwd_to_phenthau(), get_date(), get_formalArgs(), get_time(), load_test(), remove_false(), set_attributes(), subset_time(), timename()

Convert dataframe to SpatRaster list

Description

Internal function to create a SpatRaster list out of a dataframe.

By default the dataframe should have a date column called "date". The function is transferring each column into a list object and this object to a 1 col, 1 row nrow nlyr SpatRaster.

Usage

convert_df_to_srl(x, date_col = "date")

Arguments

Value

SpatRaster list (tmean, tmax, tmin) - numeric - with time attribute

See Also

Other SpatRaster transformation: convert_doy_to_logical(), convert_hour_to_meanminmax(), convert_logical_to_doy(), convert_logical_to_time(), convert_sr_to_cvec()

Convert day of year (doy) to logical

Description

Convert day of year (doy) to logical

Usage

convert_doy_to_logical(x, from, to, by = "days")

Arguments

Value

SpatRaster - logical - TRUE/FALSE before_doy/equal_after_doy

See Also

Other SpatRaster transformation: convert_df_to_srl(), convert_hour_to_meanminmax(), convert_logical_to_doy(), convert_logical_to_time(), convert_sr_to_cvec()

Convert hourly DWD temperature data to PHENTHAUproc input

Description

Convert hourly DWD temperature data to PHENTHAUproc input

Usage

convert_dwd_to_phenthau(x)

Arguments

Value

A dataframe with date, hour and mean air temperature (tmean).

See Also

Other Helper: call_function(), check_data_with_params(), check_dimension_and_time(), get_date(), get_formalArgs(), get_time(), load_test(), remove_false(), set_attributes(), subset_time(), timename()

Convert hourly to daily

Description

Convert hourly to daily

Usage

convert_hour_to_meanminmax(x)

Arguments

Value

SpatRaster list (tmean, tmax, tmin) - numeric - with time attribute

See Also

Other SpatRaster transformation: convert_df_to_srl(), convert_doy_to_logical(), convert_logical_to_doy(), convert_logical_to_time(), convert_sr_to_cvec()

Convert logical to day of year (doy)

Description

Converts logical SpatRaster with time attribute and one layer per day to single layer with the day of the year (doy) where the layer is 1/TRUE first.

Usage

convert_logical_to_doy(x, from = NULL, to = NULL)

Arguments

Value

SpatRaster - numeric - day of year

See Also

Other SpatRaster transformation: convert_df_to_srl(), convert_doy_to_logical(), convert_hour_to_meanminmax(), convert_logical_to_time(), convert_sr_to_cvec()

Convert logical to time

Description

Converts logical SpatRasters to single layer SpatRaster with timeserialnumber as value for first TRUE

Usage

convert_logical_to_time(x)

Arguments

Value

SpatRaster - numeric

See Also

Other SpatRaster transformation: convert_df_to_srl(), convert_doy_to_logical(), convert_hour_to_meanminmax(), convert_logical_to_doy(), convert_sr_to_cvec()

Convert SpatRaster to characer vector

Description

Transform local PHENTHAUproc results back from SpatRaster to vector format

Usage

convert_sr_to_cvec(x)

Arguments

Value

vector - Date as character

See Also

Other SpatRaster transformation: convert_df_to_srl(), convert_doy_to_logical(), convert_hour_to_meanminmax(), convert_logical_to_doy(), convert_logical_to_time()

Get Date format out of numeric year and monthday character value

Description

Get Date format out of numeric year and monthday character value

Usage

get_date(year, monthday, prevyear = FALSE)

Arguments

See Also

Other Helper: call_function(), check_data_with_params(), check_dimension_and_time(), convert_dwd_to_phenthau(), get_formalArgs(), get_time(), load_test(), remove_false(), set_attributes(), subset_time(), timename()

Get formalArgs of a function from a list of parameter

Description

Get formalArgs of a function from a list of parameter

Usage

get_formalArgs(p, f)

Arguments

See Also

Other Helper: call_function(), check_data_with_params(), check_dimension_and_time(), convert_dwd_to_phenthau(), get_date(), get_time(), load_test(), remove_false(), set_attributes(), subset_time(), timename()

Get legend for PHENTHAUproc models

Description

Get legend for PHENTHAUproc models

Usage

get_legend(x = "stages")

Arguments

Value

dataframe with ID, category and colors

See Also

Other Main: mortality(), parameter(), phenology(), phenthau()

Examples

# return legend for development stages
get_legend("stages")

Get time attribute

Description

Get time attribute

Usage

get_time(x)

Arguments

Value

If x is a data.frame returns the time column, else it returns the time attribute of a SpatRaster or the shared time attribute of multiple SpatRaster.

See Also

Other Helper: call_function(), check_data_with_params(), check_dimension_and_time(), convert_dwd_to_phenthau(), get_date(), get_formalArgs(), load_test(), remove_false(), set_attributes(), subset_time(), timename()

Load test data

Description

Load test data

Usage

load_test(type = "SpatRaster")

Arguments

Value

data.frame/SpatRaster list with test data

See Also

Other Helper: call_function(), check_data_with_params(), check_dimension_and_time(), convert_dwd_to_phenthau(), get_date(), get_formalArgs(), get_time(), remove_false(), set_attributes(), subset_time(), timename()

load SpatRaster - budswelling - day of year - 16 Rasterpoints around FVA 2023

Description

load SpatRaster - budswelling - day of year - 16 Rasterpoints around FVA 2023

Usage

load_test_bs()

load dataframe - daily tmean tmin tmax - Freiburg 2020

Description

load dataframe - daily tmean tmin tmax - Freiburg 2020

Usage

load_test_day()

load dataframe - hourly tmean - Freiburg 2023

Description

load dataframe - hourly tmean - Freiburg 2023

Usage

load_test_hour()

load SpatRaster list - daily - 16 Rasterpoints around FVA 2020

Description

load SpatRaster list - daily - 16 Rasterpoints around FVA 2020

Usage

load_test_srl()

load SpatRaster - hourly - 16 Rasterpoints around FVA 2022 - 2023

Description

load SpatRaster - hourly - 16 Rasterpoints around FVA 2022 - 2023

Usage

load_test_srl_hour()

Local daily Weather Station data from Freiburg

Description

A dataset containing daily mean, max and min temperatures for Freiburg from 2019-09-01 to 2020-09-30. Downloaded from opendata.dwd.de and preprocessed.

Format

A data frame with 396 rows and 4 variables

Details

The dataset can be loaded using load_test("day").

Stations_id: 01443 Stationsname: Freiburg

The variables are as follows:

• date in year-month-day - character

• tmean daily mean temperature in °C - numeric

• tmax daily max temperature in °C - numeric

• tmin daily min temperature in °C - numeric

Source

https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/daily/kl/historical/

Local hourly Weather Station data from Freiburg

Description

A dataset containing hourly temperatures for Freiburg from 2019-09-01 to 2022-12-31. Downloaded from opendata.dwd.de and preprocessed.

Format

A data frame with 29232 rows and 3 variables

Details

The dataset can be loaded using load_test("hour").

Stations_id: 01443 Stationsname: Freiburg

The variables are as follows:

• date in year-month-day - character

• tmean hourly mean temperature in °C - numeric

Source

https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/hourly/air_temperature/historical/

Calculating starvation related mortality of Thaumetopoea processionea

Description

Calculating the starvation-related mortality rate of L1 larvae, dependent on degree days from the first hatch to feeding start (bud swelling of the host tree Quercus robur). All already available parameter settings can be shown with parameter().

Usage

mortality(
  x,
  par_budswelling = "quercus_robur_clone256_type1",
  par_hatch = c("custers", "wagenhoff", "meurisse"),
  def_hatch = "first",
  last = TRUE
)

Arguments

Value

SpatRaster with mortality in %.

See Also

Other Main: get_legend(), parameter(), phenology(), phenthau()

Examples

srl <- load_test()
mortality(srl)

Create parameter list

Description

See all available models with parameter: parameter() model can be a single model or a model collection described in "Default settings". Return a data.frame with all model options: parameter()

Default Settings

The default parameter lists for different data input are: "dailymean": Regional PHENTHAUproc described in Halbig et al. 2024 for daily mean temperature data "hour": "Local PHENTHAUproc described in Halbig et al. 2024 for daily hourly temperature data "dailymeanminmax": PHENTHAUproc adapted to DWD Data for daily mean, min and max temperature data

Columns

model: model parametrisation: parametrisation method: method used to calculate effective temperatures ts_start: first day to calculate effective temperatures ts_end last day to calculate effective temperatures (Default 30. Sept) ts_prevyear: If True calculation of effective temperatures starts in previous year. (i.e. wagenhoff) ldt: lower development threshold cf_dependent: Is model cold/frost dependent cf_start: first day to calculate cold/frost days cf_end: last day to calculate cold/frost days cf_prevyear: If True calculation of cold/frost days starts in previous year. cf_temp: tmean" for cold days and "tmin" for frost days cf_limit: threshold for cold/frost days set: formula to calculate sum of effective temperatures a: parameter for set b: parameter for set

Usage

parameter(model = NULL, parametrisation = NULL, year = NULL, first = TRUE)

Arguments

Value

If no argument is specified returns a list of parameter (used inside phenthau function). Otherwise returns available parameter for given model, parametrisation and year

See Also

Other Main: get_legend(), mortality(), phenology(), phenthau()

Examples

# Default parameter list for daily mean, min and max temperature data:
parameter("dailymeanminmax")

# overview dataframe with all available parameter sets
parameter("all")

# all hatch model parameter
parameter("hatch")

# return parameter necessary for calculation
parameter("hatch", "custers", 2020)

Calculate phenological events

Description

Using daily mean or min and max temperature data, the function calculates the temperature-dependent development stages of OPM or the bud stages (bud swelling and leaf unfolding) of its host tree Quercus robur.

The default settings correspond to the model described by Halbig et al. 2024. Additional parametrizations are provided but have not yet been tested.

Halbig et al. 2024 It follows 4 different steps:

• a) Calculating and summing up cold days or frost days. (Cold days are defined as days with a mean temperature below ldt (lower development threshold), while frost days are all days with a min temperature below ldt). Hatch dependent development stages need a hatch raster (hatch happened 1 or not 0) for each day

• b) Calculating degree days with the single sine method of Baskerville & Emin, 1969 or simple summing up tmean temperatures over ldt.

• c) Calculating the needed sum of effective temperatures for the development stage

• d) Comparing degree days with the needed sum of effective temperatures

Usage

phenology(
  x,
  model,
  parametrisation = NULL,
  year = NULL,
  hatch = NULL,
  return_date = TRUE,
  ...
)

Arguments

Value

If return_date is TRUE returns single layered SpatRaster with time serial number (first occurence of phenological event). If return_date is FALSE returns a one layer per day SpatRaster type logical with phenological event occurred/not TRUE/FALSE.

Author(s)

Bachfischer Lorenz, Department of Forest Protection FVA (2024) lorenz.bachfischer@posteo.de

References

Halbig et al. 2014: Halbig, P., Stelzer, A. S., Baier, P., Pennerstorfer, J., Delb, H., & Schopf, A. (2024). PHENTHAUproc–An early warning and decision support system for hazard assessment and control of oak processionary moth (Thaumetopoea processionea). Forest Ecology and Management, 552, 121525
Baskerville & Emin 1969: Baskerville, G. L., & Emin, P. (1969). Rapid estimation of heat accumulation from maximum and minimum temperatures. Ecology, 50(3), 514-517. (doi:10.2307/1933912)
Menzel 1997: Menzel, A. (1997). Phänologie von Waldbäumen unter sich ändernden Klimabedingungen: Auswertung der Beobachtungen in den internationalen phänologischen Gärten und Möglichkeiten der Modellierung von Phänodaten. Frank.

See Also

Other Main: get_legend(), mortality(), parameter(), phenthau()

Examples

## SpatRaster
srl <- load_test()

# Calculating bud swelling for our raster example
budswelling <- phenology(srl,
                         model = "budswelling",
                         parametrisation = "quercus_robur_clone256_type1",
                         year = 2020)

Calculate PHENTHAUproc model

Description

"phenthau" implements the early warning system PHENTHAUproc created by Halbig et al. 2024 in R.

Usage

phenthau(x, params = NULL, def_hatch = "first", budswelling = NULL)

Arguments

Details

Overview

phenthau function combines multiple phenology models:

• bud swelling & leaf unfolding of Quercus robur or Quercus petraea(still in development)

• hatch of oak processionary moth using 3 different hatch models (Custers 2003, Wagenhoff et al. 2014 and Meurisse et al. 2012)

• development stages of OPM (0 egg - 8 adult)

Input requirements

For different input type different parameter sets are needed. If params is not provided it will be selected dependent on datatype and names(x) so follow the name convention!

daily raster input with tmean, tmin and tmax:

• a list of 3 named objects: "tmean", "tmin" and "tmax"

• each object is a SpatRaster with one layer per day

• terra::time for each SpatRaster has to be set to Date -> parameter("dailymeanminmax", year)

daily raster input with tmean:

• a SpatRaster

• one layer per day

• terra::time has to be set to Date

• params has to be set manually! -> parameter("dailymean", year)

hourly raster input:

• a SpatRaster

• one layer per hour

• terra::time has to be set to Date -> parameter("hour", year)

daily data.frame input with tmean, tmin and tmax:

• a data.frame with 4 columns: "date", "tmean", "tmin", "tmax"

• one row per day -> parameter("dailymeanminmax", year)

daily data.frame input with tmean:

• a data.frame with 2 columns: "date", "tmean"

• one row per day

• "date" column is.Date -> parameter("dailymean", year)

hourly data.frame input:

• a data.frame with 2 columns: "date", "hour", "tmean"

• 24 rows per day

• "date" column is.Date -> parameter("hour", year)

Parametrisation

Additional parametrization is provided but has not yet been tested.

Use parameter() to return a data.frame with all possible parametrization options or choose a model. The default is "dailymeanminmax" and not dependent on the data input anymore.

• "dailymean": Regional PHENTHAUproc how described in Halbig et al. 2024 for daily mean temperature data

• "hour": Local PHENTHAUproc how described in Halbig et al. 2024 for hourly temperature data

• "dailymeanminmax": PHENTHAUproc for daily mean, max and min temperature data adapted to open access DWD raster data

Output

Regional Output:
A list with all model calculations as SpatRaster objects:

• stages: SpatRaster with one layer per day - numeric - values from 0-8 (0 egg stage - 8 adult stage). -> use get_legend("stages") to show id/cover/colors

• custers/wagenhoff/meurisse - one layer per day - logical - TRUE/FALSE for hatch or no hatch

• budswelling: one layer per day - logical - TRUE/FALSE for budswelling or no budswelling

• leafunfolding: one layer per day - logical - TRUE/FALSE for leafunfolding or no leafunfolding -> plot the first day of a logical SpatRaster with plot_date()

• mortality: one layer - integer - mortality in %

• ppa_biocide: one layer per day - numeric - 0 application of plant protection agents (PPA) and biocides not yet effective, 1 application effective, 2 appliaction not effective anymore

Local Output:
A data.frame with two columns:

• model: name of model

• date: date of first appearing of event

Presentation

Regional Output:

• plot_stages is a wrapper around terra::plot to preset legend, names, colors and day to plot.

• plot_date is a wrapper around terra::plot to plot the date of first TRUE in multiple layered SpatRaster

Local Output:

• plot_station_step is for local weather station data and creates a stepwise graph for the development stages

Value

data.frame or list of SpatRaster with all PHENTHAUproc model outputs (see Details)

Author(s)

Bachfischer Lorenz, Department of Forest Protection FVA (2024) lorenz.bachfischer@posteo.de

References

Halbig et al. 2024: Halbig, P., Stelzer, A. S., Baier, P., Pennerstorfer, J., Delb, H., & Schopf, A. (2024). PHENTHAUproc–An early warning and decision support system for hazard assessment and control of oak processionary moth (Thaumetopoea processionea). Forest Ecology and Management, 552, 121525
Custers 2003: Custers, C. (2003). Climate change and trophic synchronisation. English Wageningen UR, Chairgroup Environmental Systems Analysis.
Wagenhoff et al. 2014: Wagenhoff, E., Wagenhoff, A., Blum, R., Veit, H., Zapf, D., & Delb, H. (2014). Does the prediction of the time of egg hatch of Thaumetopoea processionea (Lepidoptera: Notodontidae) using a frost day/temperature sum model provide evidence of an increasing temporal mismatch between the time of egg hatch and that of budburst of Quercus robur due to recent global warming?. European Journal of Entomology, 111(2).
Meurisse et al. 2012: Meurisse, N., Hoch, G., Schopf, A., Battisti, A., & Grégoire, J. C. (2012). Low temperature tolerance and starvation ability of the oak processionary moth: implications in a context of increasing epidemics. Agricultural and forest entomology, 14(3), 239-250.

See Also

Other Main: get_legend(), mortality(), parameter(), phenology()

Examples

srl <- load_test()
phen <- phenthau(srl)

Plot SpatRaster with date serial number/time attribute

Description

A wrapper around terra::plot to show the time serial number as a character date in the legend.

Usage

plot_date(x, breaks = NULL, ...)

Arguments

Value

A plot of a phenological event by day with legend.

See Also

Other Plot: plot_stages(), plot_station_step()

Examples

data <- load_test()
budswelling <- phenology(data, "budswelling", "quercus_robur_clone256_type1", 2020)
plot_date(budswelling)

Plot regional PHENTHAUproc - Stages

Description

A wrapper to plot the development stages of OPM with assigned names and colors

Usage

plot_stages(x, time = NULL, ...)

Arguments

Details

phenthau returns a list of SpatRasters. The stages object describes the development stages of oak processionary moth. This function plots the stages SpatRaster with the right names and colors. To get IDs, caregories and colors use get_legend("stages").

Value

A plot of the PHENTHAUproc stages of the last/chosen time with preset levels and colors.

See Also

Other Plot: plot_date(), plot_station_step()

Plot local PHENTHAUproc in a step plot

Description

Plots the development stages of OPM.

Usage

plot_station_step(x)

Arguments

Value

A plot showing local PHENTHAUproc results in a step plot.

See Also

Other Plot: plot_date(), plot_stages()

Examples

fr_df <- load_test("day")
fr <- phenthau(fr_df)

plot_station_step(fr)

Regional Weather data

Description

A dataset containing daily mean, minimum and maximum temperatures in °C. The dataset is a 4*4 pixel cutout centered at FVA from the "Hyras" dataset available at the DWD open data center (https://opendata.dwd.de/climate_environment/CDC/grids_germany/daily/hyras_de/)

Format

A list of SpatRaster

Details

The dataset can be loaded using load_test("SpatRaster").

Spatial resolution: 5 km x 5 km Projection: ETRS89 / LCC Europe (EPSG:3034) Parameter: air temperature at 2 m

The dataset is a list with three SpatRaster as objects. The time attribute for all three SpatRaster is equal.

The list objects are:

• tmean: SpatRaster with tmean daily mean temperature in °C - numeric

• tmax: SpatRaster with tmax daily mean temperature in °C - numeric

• tmin: SpatRaster with tmin daily mean temperature in °C - numeric

Regional Weather data

Description

A dataset containing hourly mean temperatures in °C. The dataset is a 4*4 pixel cutout centered at FVA provided by the Agricultural meteorology Department of the DWD.

Format

netCDF

Details

The dataset can be loaded using load_test("SpatRaster_hour").

Spatial resolution: 1 km x 1 km Projection: DHDN / 3-degree Gauss-Kruger zone 3 (EPSG:31467) Parameter: air temperature at 2 m

The dataset is a list with three SpatRaster as objects.

The list objects are:

• fva_hour: SpatRaster with hourly mean temperature in °C - numeric

Remove FALSE

Description

Set values in SpatRaster or SpatRaster list to -999 if y is FALSE

Usage

remove_false(x, y)

Arguments

See Also

Other Helper: call_function(), check_data_with_params(), check_dimension_and_time(), convert_dwd_to_phenthau(), get_date(), get_formalArgs(), get_time(), load_test(), set_attributes(), subset_time(), timename()

Set attributes for SpatRaster

Description

Set attributes for SpatRaster

Usage

set_attributes(x, type)

Arguments

See Also

Other Helper: call_function(), check_data_with_params(), check_dimension_and_time(), convert_dwd_to_phenthau(), get_date(), get_formalArgs(), get_time(), load_test(), remove_false(), subset_time(), timename()

Subset data.frame/SpatRaster/List within from and end time

Description

Subset data.frame/SpatRaster/List within from and end time

Usage

subset_time(x, from, to)

Arguments

See Also

Other Helper: call_function(), check_data_with_params(), check_dimension_and_time(), convert_dwd_to_phenthau(), get_date(), get_formalArgs(), get_time(), load_test(), remove_false(), set_attributes(), timename()

Create a name with time stamp

Description

Create a name with time stamp

Usage

timename(x, model, desc = NULL)

Arguments

See Also

Other Helper: call_function(), check_data_with_params(), check_dimension_and_time(), convert_dwd_to_phenthau(), get_date(), get_formalArgs(), get_time(), load_test(), remove_false(), set_attributes(), subset_time()